PhD: 3D-Aware Multi-Object Tracking

• Investigates and develops robust, efficient, and real-time multi-object tracking (MOT) algorithms, with a focus on end-to-end MOT architectures such as MOTIP2 and pedestrian tracking in challenging occlusion scenarios.
• Explores amodal and 3D-aware representations to enhance tracking performance while addressing the computational and resource constraints of edge devices.
• Designs experimental methodologies and benchmarks to evaluate proposed approaches against state-of-the-art MOT methods and real-world datasets.
• Contributes to the design, implementation, and validation of prototypes, with an initial focus on monocular tracking and future extensions toward multi-camera tracking.
• Keeps up to date with advances in computer vision, multi-object tracking, amodal perception, and 3D scene understanding, identifying and transferring relevant innovations to industrial applications.
• Communicates research findings to the IDEMIA and Télécom Paris research and engineering communities, contributing to scientific publications and papers submitted to leading computer vision and machine learning venues.

Profile description:

• Solid foundations in deep learning, particularly transformer architectures (attention mechanisms, DETR-style query-based models).
• Experience with computer vision tasks: object detection, tracking, and/or 3D/geometric vision (depth estimation, neural scene representations).
• Proficiency in Python and PyTorch.
• Familiarity with vision-language / foundation models (CLIP-like contrastive models, open-vocabulary detectors) is a plus